This page contains a selection of publications. My google scholar page with a complete list can be found here.
H. Kim, S. Hansen, M. Kampffmeyer. "Tied Prototype Model for Few-Shot Medical Image Segmentation". MICCAI 2025.
Z. Sun, S. Thrun, and M. Kampffmeyer. "VMRA-MaR: An Asymmetry-Aware Temporal Framework for longitudinal Breast Cancer Risk Prediction". MICCAI 2025.
S. Thrun, S. Hansen, Z. Sun, N. Blum, S. A. Salahuddin, K. Wickstrøm, E. Wetzer, R. Jenssen, M. Stille, M. Kampffmeyer. "Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction". MICCAI 2025.
R. A. Mancisidor, R. Jenssen, S. Yu, and M. Kampffmeyer. "Aggregation of Dependent Expert Distributions in Multimodal Variational Autoencoders". ICML 2025.
Z. Xu, H. Liu, C. Lang, T. Wang, Y. Li, and M. Kampffmeyer. "A Hubness Perspective on Representation Learning for Graph-Based Multi-View Clustering". CVPR 2025.
J. Wang, S. Feng, K. K. Wickstrøm, M. Kampffmeyer. "AdaptCMVC: Robust Adaption to Incremental Views in Continual Multi-view Clustering". CVPR 2025.
M. Heinonen, B.-H. Tran, M. Kampffmeyer, M. Filippone. "Robust Classification by Coupling Data Mollification with Label Smoothing". AISTATS 2025.
H. Li, Z. Yanpeng, T. Tang, J. Song, Y. Zeng, M. Kampffmeyer, H. Xu, and X. Liang. "UniGS: Unified Language-Image-3D Pretraining with Gaussian Splatting". ICLR 2025.
J. Chen, P. Hu, X. Chang, Z. Shi, M. Kampffmeyer, X. Liang. "Sitcom-Crafter: A Plot-Driven Human Motion Generation System in 3D Scenes". ICLR 2025.
K. K. Wickstrøm, T. Brüsch, M. Kampffmeyer, R. Jenssen. "REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability". AAAI 2025.
R. Chakraborty, A. Sletten, M. Kampffmeyer, "ExMap: Leveraging Explainability Heatmaps for Unsupervised Group Robustness to Spurious Correlations", CVPR 2024.
L. Lin, Z. Jiang, X. Liang, L. Ma, M. Kampffmeyer, X. Cao, "PTUS: Photo-Realistic Talking Upper-Body Synthesis via 3D-Aware Motion Decomposition Warping", AAAI 2024.
H. Li, H. Dong, H. Jia, D. Huang, M. C Kampffmeyer, L. Lin, X. Liang, "Coordinate Transformer: Achieving Single-stage Multi-person Mesh Recovery from Videos", ICCV 2023.
X. Zhang, B. Yang, M. C Kampffmeyer, W. Zhang, S. Zhang, G. Lu, L. Lin, H. Xu, X. Liang, "DiffCloth: Diffusion Based Garment Synthesis and Manipulation via Structural Cross-modal Semantic Alignment", ICCV 2023.
D. J Trosten, S. Løkse, R. Jenssen, M. C Kampffmeyer, "On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering", CVPR 2023.
D. J Trosten, R. Chakraborty, S. Løkse, K. K Wickstrøm, R. Jenssen, M. C Kampffmeyer, "Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings", CVPR 2023.
D. Singh, A. Boubekki, R. Jenssen, M. C Kampffmeyer, "Supercm: Revisiting Clustering for Semi-Supervised Learning", ICASSP 2023.
S. Gautam, A. Boubekki, S. Hansen, S. A. Salahuddin, R. Jenssen, M. MC Höhne, and M. Kampffmeyer, "ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model", NeurIPS 2022.
Z. Huang, H. Li, Z. Xie, M. Kampffmeyer, Q. Cai, and X. Liang, "Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning", NeurIPS 2022.
N. Dong, M. Kampffmeyer and I. Voiculescu, "Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images", MICCAI 2022.
X. Zhang, Y. Sha, M. Kampffmeyer, Z. Xie, Z. Jie, H. Chengwen, J. Peng and X. Liang, "ARMANI: Part-level Garment-Text Alignment for Unified Cross-Modal Fashion Design", ACM MM 2022.
X. Dong, X. Zhan, Y. Wu, Y. Wei, M. Kampffmeyer, X. Wei, M. Lu, Y. Wang and X. Liang, "M5Product: Self-harmonized Contrastive Learning for E-commercial Multi-modal Pretraining", CVPR 2022.
S. Gautam, M. M. C. Höhne, S. Hansen, R. Jenssen, and M. Kampffmeyer, "Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation", ISBI 2022.
Z. Xie, Z. Huang, F. Zhao, H. Dong, M. Kampffmeyer and X. Liang, "Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN", NeurIPS 2021.
N. Dong, M. Kampffmeyer and I. Voiculescu, "Self-supervised multi-task representation learning for sequential medical images", ECML 2021.
F. Zhao, Z. Xie, M. Kampffmeyer, H. Dong, S. Han, T. Zheng, T. Zhang and X. Liang, "M3d-vton: A monocular-to-3d virtual try-on network", ICCV 2021.
Z. Xie, X. Zhang, F. Zhao, H. Dong, M. Kampffmeyer, H. Yan, and X. Liang, "Was-vton: Warping architecture search for virtual try-on network", ACM MM 2021.
D. J. Trosten, S. Løkse, R. Jenssen and M. Kampffmeyer, "Reconsidering Representation Alignment for Multi-view Clustering", CVPR 2021.
D. J. Trosten, R. Jenssen and M. Kampffmeyer, "Reducing Objective Function Mismatch in Deep Clustering with the Unsupervised Companion Objective", Proceedings of the Northern Lights Deep Learning Workshop 2021.
V. N. Nguyen, S. Løkse, K. Wickstrøm, M. Kampffmeyer, D. Roverso and R. Jenssen, "SEN: A Novel Dissimilarity Measure for Prototypical Few-Shot Learning Networks", European Conference on Computer Vision 2020.
Q. Liu, M. Kampffmeyer, R. Jenssen and A.-B. Salberg, "Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation", IEEE Computer Vision and Pattern Recognition Workshop 2020.
Mang Tik Chiu, Xingqian Xu, M.Kampffmeyer and others, "The 1st Agriculture-Vision Challenge: Methods and Results", IEEE Computer Vision and Pattern Recognition Workshop 2020.
Q. Liu, M. Kampffmeyer, R. Jenssen and A.-B. Salberg, "Self-Constructing Graph Convolutional Networks for Semantic Labeling", IGARSS 2020. [Preprint]
C. Choi, F.M. Bianchi, M. Kampffmeyer, and R. Jenssen, "Short-Term Load Forecasting with Missing Data using Dilated Recurrent Attention Networks", Proceedings of the Northern Lights Deep Learning Workshop, 2020.
M. Kampffmeyer, Y. Chen, X. Liang, H. Wang, Y. Zhang and E. P. Xing, "Rethinking Knowledge Graph Propagation for Zero-Shot Learning", CVPR 2019. [Link][Preprint][Code]
Q. Liu, M. Kampffmeyer, R. Jenssen and A.-B. Salberg, "Road mapping in lidar images using a joint-task dense dilated convolutions merging network", IGARSS 2019.
Y. Zhang, M. Kampffmeyer, X. Zhao and M. Tan, "DTR-GAN: Dilated Temporal Relational Adversarial Network for Video Summarization", ACM TURC 2019.
Q. Liu, M. Kampffmeyer, R. Jenssen and A.-B. Salberg. "Dense dilated convolutions merging network for semantic mapping of remote sensing images", International Joint Urban Remote Sensing Event (JURSE), 2019.
D. J. Trosten, A. S. Strauman, M. Kampffmeyer and R. Jenssen, "Recurrent Deep Divergence-based Clustering for simultaneous feature learning and clustering of variable length time series", International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019. [Preprint]
N. Dong, M. Kampffmeyer, X. Liang, Z. Wang, D. Wei and E. P. Xing, "Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images", Workshop on Deep Learning in Medical Image Analysis, MICCAI 2018. [Preprint]
Y. Zhang, M. Kampffmeyer, X. Liang, M. Tan, E. P. Xing, "Query-Conditioned Three-Player Adversarial Network for Video Summarization", British Machine Vision Conference 2018. [Preprint]
K. W. Wickstrøm, M. Kampffmeyer and R. Jenssen, "Uncertainty Modeling And Interpretability In Convolutional Neural Networks For Polyp Segmentation", IEEE International Workshop on Machine Learning for Signal Processing 2018. [Preprint]
N. Dong, M. Kampffmeyer, X. Liang, Z. Wang, D. Wei and E. P. Xing, "Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio", Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI) 2018. [Preprint]
M. A. Hansen, K. Ø. Mikalsen, M. Kampffmeyer, C. Soguero-Ruiz and R. Jenssen, "Towards Deep Anchor Learning", IEEE International Conference on Biomedical and Health Informatics 2018. [Link]
A. S. Strauman, F. M. Bianchi, K. Ø. Mikalsen, M. Kampffmeyer, C. Soguero-Ruiz and R. Jenssen, "Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks", IEEE International Conference on Biomedical and Health Informatics 2018. [Link][Preprint]
M. Kampffmeyer, S. Løkse, F. M. Bianchi, L. Livi, A. Salberg and R. Jenssen "Deep Divergence-Based Clustering", IEEE International Workshop on Machine Learning for Signal Processing 2017. [Link][Preprint][Presentation]
M. Kampffmeyer, A.-B. Salberg and R. Jenssen, "Urban Land Cover Classification with Missing Data Using Deep Convolutional Neural Networks", IEEE International Geoscience and Remote Sensing Symposium 2017. [Link]
M. Kampffmeyer, S. Løkse, F. M. Bianchi, R. Jenssen and L. Livi, "Deep Kernelized Autoencoders", Scandinavian Conference on Image Analysis 2017. [Link][Preprint][Presentation]
A.-B. Salberg, Ø. D. Trier and M. Kampffmeyer, "Large-Scale Mapping of Small Roads in Lidar Images Using Deep Convolutional Neural Networks", Scandinavian Conference on Image Analysis 2017. [Link]
F. M. Bianchi, M. Kampffmeyer, E. Maiorino and R. Jenssen, "Temporal Overdrive Recurrent Neural Network". International Joint Conference on Neural Networks 2017. [Link][Preprint][Presentation]
J. N. Myhre, M. Kampffmeyer, R. Jenssen, "Density ridge manifold traversal", IEEE International Conference on Acoustics, Speech and Signal Processing 2017. [Link]
M. Kampffmeyer, A.-B. Salberg and R. Jenssen, "Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks", IEEE Computer Vision and Pattern Recognition Workshop on Visual Analysis of Satellite to Street Imagery 2016. [Link]
J. N. Myhre, M. Kampffmeyer and R. Jenssen, "Ambient space manifold learning using density ridges", Geometry in Machine Learning Workshop, International Conference on Machine Learning 2016. [Link]
S. Hansen, S. Gautam, S. A Salahuddin, M. Kampffmeyer, R. Jenssen, "ADNet++: A few-shot learning framework for multi-class medical image volume segmentation with uncertainty-guided feature refinement", Medical Image Analysis, 2023.
J. Lederer, M. Gastegger, K. T Schütt, M. Kampffmeyer, K.-R. Müller, O. T Unke, "Automatic identification of chemical moieties", Physical Chemistry Chemical Physics, 2023.
K. K Wickstrøm, D. J Trosten, S. Løkse, A. Boubekki, K. Ø Mikalsen, M. C Kampffmeyer, R. Jenssen, "Relax: Representation learning explainability", International Journal of Computer Vision, 2023.
S. Gautam, M. MC Höhne, S. Hansen, R. Jenssen, M. Kampffmeyer, "This looks more like that: Enhancing self-explaining models by prototypical relevance propagation", Pattern Recognition, 2023.
S. Hansen, S. Gautam, R. Jenssen and M. Kampffmeyer, "Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels", Medical Image Analysis, 2022.
L. Luppino, M. Hansen, M. Kampffmeyer, F. Bianchi, G. Moser, R. Jenssen and S. Anfinsen, "Code-aligned autoencoders for unsupervised change detection in multimodal remote sensing images", IEEE Transactions on Neural Networks and Learning Systems, 2022.
R. A Mancisidor, M. Kampffmeyer, K. Aas and R. Jenssen, "Generating customer’s credit behavior with deep generative models", Knowledge-Based Systems, 2022.
N. Dong, M. Kampffmeyer, I. Voiculescu and E. Xing, "Negational symmetry of quantum neural networks for binary pattern classification", Pattern Recognition, 2022.
K. Wickstrøm, M. Kampffmeyer, K. Ø. Mikalsen, R. Jenssen, "Mixing up contrastive learning: Self-supervised representation learning for time series", Pattern Recognition Letters, 2022.
A. Boubekki, M. Kampffmeyer, U. Brefeld and R. Jenssen, "Joint optimization of an autoencoder for clustering and embedding". Machine Learning, 2021.
N. Dong, M. Kampffmeyer, X. Liang, M. Xu, I. Voiculescu and E. Xing, "Towards robust partially supervised multi-structure medical image segmentation on small-scale data". Applied Soft Computing, 2021.
Q. Liu, M. Kampffmeyer, R. Jenssen and A. B. Salberg, "Self-constructing graph neural networks to model long-range pixel dependencies for semantic segmentation of remote sensing images". International Journal of Remote Sensing, 2021.
C. Choi, M. Kampffmeyer, N. O. Handegard, A. B. Salberg, O. Brautaset, L. Eikvil and R. Jenssen, "Semi-supervised target classification in multi-frequency echosounder data". ICES Journal of Marine Science, 2021.
L. Luppino, M. Kampffmeyer, F. M. Bianchi, G. Moser, S. B. Serpico, R. Jenssen, S. Anfinsen, "Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection", IEEE Transactions on Geoscience and Remote Sensing 2021. [Preprint]
R. A. Mancisidor, M. Kampffmeyer, K. Aas and R. Jenssen. Learning latent representations of bank customers with the Variational Autoencoder. Expert Systems with Applications, 2021.
S. Hansen, S. Kuttner, M. Kampffmeyer, T.V. Markussen, R. Sundset, R., S.K. Øen, L.Eikenes and R. Jenssen. Unsupervised supervoxel-based lung tumor segmentation across patient scans in hybrid PET/MRI. Expert Systems with Applications, 2021.
R. A. Mancisidor, M. Kampffmeyer, K. Aas and R. Jenssen, "Deep generative models for reject inference in credit scoring.", Knowledge-Based Systems, 2020.
Q. Liu, M. Kampffmeyer, R. Jenssen and A.-B. Salberg "Dense Dilated Convolutions Merging Network for Land Cover Classification", IEEE Transactions on Geoscience and Remote Sensing, 2020.
K. Wickstrøm, K. Ø. Mikalsen, M. Kampffmeyer, A. Revhaug and R. Jenssen. Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series. IEEE Journal of Biomedical and Health Informatics, 2020.
A. Ordoñez, L. Eikvil, A.-B. Salberg, A. Harbitz, S. Murray and M. Kampffmeyer "Explaining decisions of deep neural networks used for fish age prediction.", PLOS ONE, 2020.
K. Wickstrøm, M. Kampffmeyer and R. Jenssen "Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps", Medical Image Analysis, 2020.
M. Kampffmeyer, S. Løkse, F. M. Bianchi, L. Livi, A.-B. Salberg and R. Jenssen, "Deep Divergence-Based Approach to Clustering", Neural Networks, 2019. [Link][Preprint]
M. Kampffmeyer, N. Dong, X. Liang, Y. Zhang and E. P. Xing, "ConnNet: A Long-Range Relation-Aware Pixel-Connectivity Network", IEEE Transactions on Image Processing, 2019. [Link][Preprint]
Y. Zhang, M. Kampffmeyer, X. Liang, D. Zhang, M. Tan and E. P. Xing, "Dilated temporal relational adversarial network for generic video summarization", Multimedia Tools and Applications 2019.
F. M. Bianchi, L. Livi, K. Ø. Mikalsen, M. Kampffmeyer and R. Jenssen "Learning representations of multivariate time series with missing data". Pattern Recognition 2019. [Link][Preprint]
Y. Zhang, M. Kampffmeyer, X. Zhao and M. Tan, "Deep Reinforcement Learning for Query-Conditioned Video Summarization". Applied Sciences, 2019. [Link]
M. Kampffmeyer, S. Løkse, F. M. Bianchi, R. Jenssen and L. Livi, "The Deep Kernelized Autoencoder", Applied Soft Computing, 2018. [Link][Preprint]
M. Kampffmeyer, A.-B. Salberg and R. Jenssen, "Urban Land Cover Classification with Missing Data Modalities Using Deep Convolutional Neural Networks", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018. [Link][Preprint]